I’ve got a new draft paper out with a host of colleagues here at the OII entitled Does Campaigning on Social Media Make a Difference? Evidence from candidate use of Twitter during the 2015 and 2017 UK Elections. There’s an enormous volume of research on the activities of politicians on social media, especially around election time, but not a lot of it has actually addressed whether this activity ‘makes a difference’, i.e. helps to win votes. Part of the reason for this is that measuring ‘campaign effects’ is quite difficult (unless you can convince campaigns themselves to participate in field experiments) and most of the data is purely cross-sectional which means a host of causality problems in this type of context.

Our study improves the situation by taking advantage of the fact that the UK has recently had two general elections in quick succession, and a considerable proportion of politicians (around 800 in fact) fought in both of them. This allowed us to create a panel dataset of politician social media use (in particular their Twitter activity) and electoral outcomes, which allows for much stronger causal claims (essentially we look at whether a change in the level of social media use by candidates was correlated with a change in vote share outcomes, controlling for factors such as the party they belong to).

The results were pretty interesting – we found a large amount of Twitter activity, spread throughout the country (see graphics), which support the idea that social media use is now a normal part of political campaigns. However the level of effort did vary quite a lot and this allowed us to explore our key interest, where we did indeed find that increasing Twitter activity correlates with increased levels of votes, even in this pretty strong panel data design. So – some good supporting evidence that politicians aren’t wasting their time on social media!

Over the last decade or so there has been an explosion of research interest in the area of measuring (and forecasting) of traffic and commuting patterns. Part of this is driven by ever increasing human mobility: in 2016 alone, people in the UK travelled a collective 800 billion kilometres [PDF], more than 60% of which was by car, and congestion on these networks costs billions of pounds a year. But also driving the research agenda is the emergence of a wide variety of new forms of data (which has built on and supplemented more traditional magnetic loop technologies): such as data re-purposed from mobile phone records, or collected through IoT enabled smart sensors, or emerging from freely contributed traces to social media platforms. These data sources offer huge potential to improve on existing methods of data collection, such as hated transport census (see picture).

As part of a research project entitled NEXUS: Real Time Data Fusion and Network Analysis for Urban Systems (funded by InnovateUK), myself and a team of researchers at the OII have been looking into some of these possibilities. Our first paper on the subject, entitled “Estimating Local Commuting Patterns from Geolocated Twitter Data“, has just been published in EPJ Data Science. The paper addresses the extent to which we can make use of geolocated Twitter data to estimate commuting flows between local authorities (you can have a play with some of the underlying data using the map below, which shows census commuting figures and Twitter based estimates for local authorities around Britain).

We draw two main conclusions from the paper. First we show that, making use of heuristics for mapping individuals making geolocated tweets to home and work areas, we can use Twitter to produce accurate representations of the overall structure of commuting in mainland Great Britain; estimates which improve considerably on other ‘low information’ methods of estimating commuting flows (we compared estimates in particular to the popular radiation model). Second, and probably most importantly, we show that these results are not particularly sensitive to demographic characteristics. When looking at commuting flows broken down by gender, age group and social class, we found that Twitter still offered reasonable estimations for all of these sub-categories. We think this is important because a key concern about using social media data for this type of proxy estimation is the extent to which the ‘demographic bias’ in social media users (who are often younger, better educated and wealthier than the population average) might also result in biased predictions (for example, better prediction of the travel patterns of younger people). We show that, at least in our context, this is not the case.

What’s next? There is plenty more to explore in this research area: looking at whether predictions can be made more granular, or perhaps whether sentiment from social media can be worked in, or whether other platforms can also contribute. We will also start to work on some other data sources, making use of some of the exciting datasets being made available by places like the ADRN and CDRC.

I have a new article out in the Journal of Communication which analyses which types of news get shared the most. Based on articles published in BBC news, the research shows that even though readership drives sharing in general, certain types of articles lend themselves more to being shared than others.

The graphic above gives a glimpse of some of the results, by visualising the relationship between reading and sharing for different categories of news article. We can see that reading and sharing are not in a linear relationship: rather some types of article are well shared but not well read, and vice versa. For example, stories about technology and social welfare seem to be shared more, whilst stories about violent crime and accidents are shared less. This creates a social “news gap” (following Boczkowski and Mitchelstein’s traditional news gap) whereby peoples preferences for sharing and their preferences for reading diverge. I suggest that, as more and more people start to consume news on social media, the implications of this become potentially more profound: as social media starts to filter out certain types of news whilst emphasising others.

I am giving a presentation tomorrow at the IJPP conference here in Oxford. It’s being hosted by the Reuters Institute who are world leaders in the study of contemporary news organisations, and I’m really excited to be going.

Together with Scott Hale I am giving a presentation on the “history” of social news. We have an 8 year long dataset (2002-2010) consisting of links to millions of news articles which we have used to trace the beginnings of social media news sharing. We are interested to know whether the types of news being shared have changed over time as social media platforms have massified; we’re also interested in looking at whether site design changes (such as bringing in sharing buttons) have had a major impact.

The project is at an early stage but the results are pretty interesting so far (to me). To give one tidbit, we show that in this large scale dataset there is only a weak correlation between sharing on Twitter and Facebook at the article level, with Twitter tending to share more sports news than Facebook (see image).

Last week we had a sort of social media hackathon in honour of the UK’s election, looking at the reaction generated on social media. We took what I believe was a fairly novel approach to the analysis, by looking at social media reaction to individual candidates in constituencies (rather than just general hashtags or party leaders). The map below shows what the election results would have been if @mentions of these local candidates had been votes

We are still digesting the data so I’m not yet sure what the main findings are really, though we did get some interesting stuff on the diverging social media “reach” of different candidates, and the way Twitter impact and vote has different relationships depending on the party.

Over the last week or so I gave a short comment on BBC South Today (as well as various radio stations) about a recent government move to allow anyone to record local council meetings and then post the results on the internet. A good idea I thought, though some councillors were apparently worried about being taken out of context (though in fairness I think the majority of councillors were also pretty supportive).

A few of the journalists asked some interesting questions about how the move might affect the style of local politics, and how it all compared with the televisation of parliament which started in the late 1980s (and which many MPs fought bitterly). Unlike parliament where televisation is pretty regulated, anyone with a phone will be able to record in council meetings, which means that councillors will have to be a lot more on their guard I suppose; though they are also likely to be the ones who do most of the filming (either of themselves or of any opponents they can catch out). There may also be a move towards more soundbites: if you are looking for coverage, the key thing is not being recorded so much as saying something which the national press want to repeat. In the 1990s, for example, Teresa Gorman suggested “cutting the goolies off” sexual offenders, rather than calling for castration, because she knew it would put her on television (see this article).

Regardless of any style changes I think if it can provide a boost to the visibility of local democracy then the move will be worth it.